ASSESSMENT OF URBAN DYNAMICS IN LAND USE AND DEMOGRPAHY USING GIS TECHNIQUES

International Research Journal of Engineering and Technology (IRJET) e-ISSN: 2395-0056
Volume: 10 Issue: 04 | Apr 2023 www.irjet.net p-ISSN: 2395-0072
© 2023, IRJET | Impact Factor value: 8.226 | ISO 9001:2008 Certified Journal | Page 1608
ASSESSMENT OF URBAN DYNAMICS IN LAND USE AND DEMOGRPAHY
USING GIS TECHNIQUES
Nivetha.M1, Sudhan Srinivas.N2
1M.plan Student ,school of planning Architecture and Design Excellence SPADE, Hindustan Institute of science of
Tecchnology and Science(HITS), Chennai,
2Assitant professor, school of planning Architecture and Design Excellence SPADE, Hindustan Institute of science of
Tecchnology and Science (HITS), Chennai,Tamilnadu
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Abstract - The process of urbanization is occurring
rapidly in poorer nations and causing significant shifts in
land use. This is linked to population expansion and poses
challenges for managing urban growth and land use. To
address these challenges, integrated urban policy
approaches and sustainable urbanization strategies are
necessary. This study uses literature reviews and case
studies from both national and international contexts to
identify various models, methods and approaches for
managing urban growth and land use. The study focuses on
sampling techniques, data collection and GIS techniques to
analyse case studies and prioritize indicators for future land
use changes. The conclusion of this dissertation highlights
the importance of adapting to various urbanization
scenarios considering the spatial and temporal scale of
change and implementing sustainable urbanization
strategies. The use of GIS tools for analysing future land use
changes leads the way forward for planning and managing
urban growth, creating properly planned cities, and
promoting the quality of life for inhabitants and the
environment. Overall, this study contributes to a deeper
understanding of the dynamics of urban growth and the role
of land use and demography in shaping the future of urban
environments
Key Words: Land use and land cover, Population,
Remote sensing, ARC GIS , LANDSAT images .
1.INTRODUCTION
Urbanization is a complex and multidimensional process
that is occurring at an unprecedented rate and scale
worldwide. As the world's population continues to grow
the cities are becoming the primary centers of economic
and social activity resulting in significant changes in land
use and patterns of urban growth. While urbanization is
happening at different rates and scales across the globe,
developing countries are experiencing particularly rapid
urbanization, which is directly linked to population
growth. The process of urbanization involves a
transformation of the natural landscape including the
conversion of rural land to urban land use resulting in the
loss of natural habitats, biodiversity, and open spaces. The
impacts of urbanization on the environment and human
health are significant and complex ranging from pollution
and resource depletion to social and economic inequality.
Managing urban growth and land use change is a critical
challenge for policymakers and planners in both
developed and developing countries. Sustainable
urbanization strategies and integrated urban policy
approaches are essential to ensure that urbanization
occurs in a manner that is socially, economically, and
environmentally sustainable. Understanding the dynamics
of urban growth and land use change taking into account
of spatial and temporal considerations is essential to
developing effective policies and strategies for managing
urban environments.
1.1 Aim
To Study Urban Dynamics using Geospatial tools such
as Geographic Information System (GIS) and Global
positioning sytem (GPS)
1.2 Objective Of The Study
The objective of this study is to gain an understanding
of the urban dynamics in major cities in both nationally
and internationally. This includes examining the land use
patterns, demographic profiles, and population densities
in these areas. Another goal of the study is to explore the
use of GIS in analysing land use patterns and identifying
future land use scenarios which can be used to inform
urban planning and management strategies.
1.3 Scope
The objective of the study is to understand the
reasons behind unplanned urban expansion in cities due
to unequal population and development growth. The study
will also focus on analysing the dynamics of land use
resulting from changing population demand.
1.4 Limitation Of The Study
The objective of the study is to understand the
reasons behind unplanned urban expansion in cities due
to unequal population and development growth. The study

will also focus on analysing the dynamics of land use
resulting from changing population demand.
1.5 Methodology
The methodology of a study refers to the approach or
method used to understand and identify various
classifications for the research. This involves a series of
stages such as selecting a research design, defining the
research problem. Proper execution of these stages is
crucial for producing reliable and valid research outcomes.
Fig 1 Methodology of the study
The search for 5 literature reviews and 8 case studies
published from 2005 to 2022 was conducted using various
databases with search terms related to land use changes
and population density analysis using GIS. The studies
were manually analysed to determine their study designs
and outcomes. A comprehensive review was undertaken
to summarize the findings from these studies.
2. Literature Study
After reviewing the literature and case studies
several key indicators related to land use changes and
population density analysis using GIS were identified.
Further study should be conducted to understand how
these indicators impact land use changes and population
density and how GIS can be used to inform decision
making in these areas.
2.1 APPROACH USED FOR LAND USE AND LAND COVER
Land use and land cover changes are important
spatial problems that require effective management and
decision-making. Fuzzy decision and Markov models have
been proposed as tools for addressing these issues [1].
Land use changes are complex phenomena influenced by
multiple factors. The analysis with co-concurrence
network can help identify the causes and effects of land
use changes and forecast future scenarios [2]. Accurate
forecasting of land use and land cover (LULC) changes is
essential for sustainable planning and management. The
use of Cellular Automata-Artificial Neural Network (CA-
ANN) and Modules to Identify Impacts of Maximum
Changes on Agricultural and Water Bodies in LULC
(MOLUSCE) can help forecast future LULC data and assess
the impacts on agricultural and water bodies over time [3].
The Land use changes using MOLUSCE, (CA-ANN),SLEUTH
to categorized the dominant and converted clasess in land
use changes [4].
To effectively manage land use and land cover changes, it
is important to recognize that these phenomena are
complex and influenced by multiple factors. Fortunately,
researchers have proposed various tools and models, such
as fuzzy decision and Markov models, co-concurrence
network analysis, CA-ANN, and MOLUSCE, that can be
used to address these issues. These tools and models can
forecast future LULC data and evaluate the impacts on
agricultural and water bodies over time. By utilizing these
models, sustainable planning and management of land use
changes can be achieved.
2.2 Different method of data collection techniques
used
Mapping land use and land cover changes is
important for understanding the spatial patterns and
dynamics of the landscape. The use of Global Positioning
System (GPS) and Geographic Resources Analysis Support
System (GRASS) Open Source Software can help to map
land use and land cover changes over a certain period [1].
Monitoring land use changes is essential for sustainable
land use planning and management. The use of Web of
Science (WOS) bibliographic database and Geographic
Information System (GIS) can help to collect data and
monitor land use changes at a range of scales [2]. The use
of Landsat images and Arc GIS overlay analysis can
provide a powerful tool for preparing and analysing LULC
data. Decision-makers can use this approach to determine
changes between LULC sub-classes and make informed
choices for sustainable land use planning and
management [3]. Monitoring urban growth and population
is essential for understanding the dynamics of
urbanization. The use of Logic Scoring Preference (LSP)
can help to identify high-density urban growth areas and
provide insights into urbanization patterns [5] .The
accurate mapping of land use is essential for sustainable
land use planning and management. The use of QGIS can
help collect and pre-process raster data to create land use
maps [4].
The use of a diverse range of tools and technologies such
as GPS, GRASS Open Source Software, WOS bibliographic
Introduction to Topic
Aim, Objective,Scope,
Limitation
Identification of
literature study
Identification of Case
study (National and
International )
Findings
Conclusion

database, GIS, Landsat images, Arc GIS overlay analysis,
LSP, and QGIS can assist in mapping and monitoring
changes in land use over time. These technologies can
provide decision-makers with valuable insights into the
spatial patterns and dynamics of the landscape, which can
inform sustainable land use planning and management
strategies. Furthermore, monitoring the growth of urban
areas and population is crucial for comprehending the
impact of urbanization on the environment and LSP can
aid in identifying regions of high-density urban growth.
Ultimately, accurate mapping of land use is fundamental to
sustainable land use planning and management with
different leveraging tools and technologies can help
achieve this objective.
2.3 FORECASTING AND EVALUATING DEMOGRAPHY
Forecasting urban growth is essential for
sustainable urban planning and management. The use of
Analytical Hierarchical Process (AHP) and Multi-Criteria
Evaluation can help for the next 10 years [1]. Urban
growth can lead to a range of social, economic, and
environmental challenges. The use of Urban Growth
Analysis (UDSA) with decision support systems and
stakeholder engagement can help upgrade land use
policies to facilitate sustainable urban development [5].
The use of a diverse range of tools and technologies such
as GPS, GRASS Open Source Software, WOS bibliographic
database, GIS, Landsat images, Arc GIS overlay analysis,
LSP, and QGIS can assist in mapping and monitoring
changes in land use over time. These technologies can
provide decision-makers with valuable insights into the
spatial patterns and dynamics of the landscape which can
inform sustainable land use planning and management
strategies. Furthermore, monitoring the growth of urban
areas and population is crucial for comprehending the
impact of urbanization on the environment and LSP can
aid in identifying regions of high-density urban growth.
Ultimately, accurate mapping of land use is fundamental to
sustainable land use planning and management, and
leveraging different tools and technologies can help
achieve this objective.
2.4 INFERENCE
Effective management and decision-making for
land use and land cover changes require accurate data
collection, forecasting, and evaluation techniques. Various
approaches have been proposed such as fuzzy decision,
Markov models, co-concurrence network, CA-ANN,
MOLUSCE, SLEUTH, GPS, GRASS, WOS, GIS, Landsat
images, Arc GIS overlay analysis, LSP and QGIS.
Furthermore, forecasting and evaluating demography are
crucial for sustainable urban planning and management.
Techniques like AHP, multi-criteria evaluation, UDSA, and
stakeholder engagement can facilitate sustainable urban
development. These methods and techniques help
decision-makers understand the spatial patterns and
dynamics of land use changes, forecast future scenarios
and make informed choices for sustainable land use
planning and management.
3. CASE STUDY ( National and International )
In order to attain through understanding the challenges
and issues concerning land use and land cover changes in
India .It is imperative to analyse national case studies.
These studies provide valuable insights into the spatial
patterns and dynamics of such changes along with the
socio-economic and environmental factors that influence
them. Furthermore, national case studies can assist in
formulating policies and making informed decisions for
sustainable land use planning and management in the
nation.
3.1 Study Area – Nellore in Andhra Pradesh, India .
The paper "Land Use and Land Cover Analysis of Nellore
Using GIS and Remote Sensing Techniques" discusses the
approaches and methods employed to study land use and
land cover changes in the Nellore district of Andhra
Pradesh, India. It explores various data collection
techniques and remote sensing in GIS tools utilized to
analyse the spatial patterns and dynamics of land use
changes [6]
This research focuses on the Nellore district in Andhra
Pradesh, India, covering an area of around 13076 sq.km
with a population density of 182 per sq.km. The study area
lies between latitude 14°30’24” – 14°38’38” N and
longitude 80°04’18”– 80°13’08”E falling in Survey of India
Toposheet No: 66B02. The eastern part of the district is
relatively fertile and prosperous, while the coastal belt,
extending about 5 to 6 km inland is sandy [6].
Fig 2 Study area of Nellore

Two image classification methods, supervised and
unsupervised, were employed in the Land Use and Land
Cover Analysis of Nellore using GIS and remote sensing
techniques. These methods were used to identify the
significant increase in agriculture and built-up areas,
which can aid in the implementation of land use schemes.
Thematic and topographic maps were created through Arc
GIS software to represent the tone, size, shape, and texture
of the land use image. The thematic maps provided
information on land use categories such as agriculture,
built-up areas, and water bodies. The topographic maps on
the other hand depicted the terrain elevation and slope of
the study area. The study also employed a change
detection analysis to identify the temporal changes in land
use and land cover patterns. This involved comparing two
sets of satellite images taken at different time periods, and
analysing the differences in land use and land cover
classes.
3.2 Study Area - Raiganj Urban Agglomeration, India
The research was conducted in the southwestern part of
Uttar Dinajpur district located in West Bengal, India. The
area spans across 110.80 square kilometers and has a
population of 199,690 individuals. Raiganj, an urban
agglomeration consisting of the Raiganj municipality,
Kasba, and Nachhratpur Katabari census town falls under
category I UAs/towns as per the 2011 census [7].
The research paper titled "Monitor Dynamics Using
Remote Sensing And GIS Techniques Of Raiganj Urban
Agglomeration, India" aims to investigate the land use and
land cover changes in the Raiganj urban agglomeration
area in India using remote sensing and GIS techniques. The
study focuses on the approach and methods used for land
use and land cover analysis including the collection of data
using various techniques and the forecasting and
evaluation of demography [7].
Fig 3 - Study area of Raiganj Urban
The study employs Kappa coefficient to analyse the land
use and land cover classes and identify the continued
expansion of built-up areas and major losses in other land
use classes. The Accuracy Assessment method is utilized to
predict accurate data and indicate the allocation of built-
up areas and higher urban sprawl. To identify urban
growth and typology, Pearson's Chi-square and Shannon
Entropy are used to analyse high growth sprawl in the
edge and beginning of the Raiganj area. The results show
that built-up areas have increased significantly in the
urban agglomeration. The study also examines the
demographic changes in the region through forecasting
and evaluation techniques. The findings indicate a
substantial population increase in the area and a
corresponding rise in demand for urban infrastructure
and services.
3.3 Study Area - Miami Area, South Florida, Usa
The study examines Miami-Dade County, the most
populous country in Florida, which is made up of 34
incorporated municipalities, cities, towns, and villages.
Miami one of the smallest major cities in the United States
covers 93.2 sq.km of land and 52.0 sq.km of water.
According to the Florida geological survey, the county's
base rock is composed of sedimentary rocks and limestone
from the Ordovician-Devonian era [8].

Fig 4 – Study Area of Miami Dade County
The research utilized linear statistical and regression
models to predict land use maps, with a specific focus on
urban built-up and vegetation areas. To analyse the color
of each picture in the land use map the study employed the
open-source color palette software "Image Color Extract
and PHP tool." A supervised classification approach was
utilized for the land use classification and the kappa
coefficient was used to assess the accuracy of the
classification. The study collected data from various
sources including satellite imagery, topographic maps and
field surveys. Additionally the study calculated the
normalized difference vegetation index (NDVI) to quantify
the vegetation cover in the study area. The land use maps
were generated using ArcGIS 10.1 software and the
analysis was conducted using SPSS 22 software. The
findings showed significant expansion of urban built-up
areas over the study period accompanied by a decline in
vegetation areas. Overall, the study underscored the
effectiveness of remote sensing and GIS techniques in
monitoring urban growth dynamics.
3.4 Study Area – Opole , Poland
The study focuses on the city of Opole, situated in the
south-western region of Poland, which serves as the
capital of Opole Province and the core of Opole
Agglomeration. As the largest economic, academic and
cultural center in the region it holds great significance as a
labor market for its residents and the surrounding areas.
The city's area was expanded to 149 Sq.km in 2017 by
including 12 new villages, ranking it 15th among Polish
districts and cities in terms of area. Previously, Opole
covered an area of 97 km2. The city is located in the Odra
River valley [9].
Fig 5 Poland, Central Europe
The study employed a supervised maximum likelihood
classification algorithm (MLC) to examine changes in land
use and cover due to unregulated development of built-up
areas. To determine future land use maps the researchers
utilized ArcGIS and SNAP software for image processing,
spatial raster analysis, and digital GIS creation. This
approach facilitated accurate mapping of land use changes
and could provide valuable insights for policymakers
when developing sustainable land use policies. In addition,
the study utilized demographic forecasting techniques to
evaluate population growth rates (PGR) and avoid a
decrease in population projections. The LCR and PGR were
applied to identify areas for potential growth and
development while reducing negative urbanization
impacts.
3.5 Inference
The study examined changes in land use and cover due to
unregulated development of built-up areas using remote
sensing and GIS techniques. The researchers utilized
supervised maximum likelihood classification algorithm
and demographic forecasting techniques to identify areas
for potential growth and development while reducing
negative urbanization impacts. The study utilized linear
statistical and regression models to predict land use maps
with a specific focus on urban built-up and vegetation
areas. It shows significant expansion of urban built-up
areas over the study period, accompanied by a decline in
vegetation areas. The study also examined the
demographic changes in the region through forecasting
and evaluation techniques indicating a substantial
population increase in the area and a corresponding rise
in demand for urban infrastructure and services.
Two image classification methods, supervised and
unsupervised were employed in the Land Use and Land
Cover Analysis of Nellore using GIS and remote sensing
techniques. Thematic and topographic maps were created
through Arc GIS software to represent the tone, size,
shape, and texture of the land use image, providing
information on land use categories such as agriculture,
built-up areas, and water bodies. The study also employed
Miami Dade

a change detection analysis to identify temporal changes
in land use and land cover patterns, comparing two sets of
satellite images taken at different time periods. The results
showed a significant increase in agriculture and built-up
areas which can aid in the implementation of land use
schemes. The study highlighted the effectiveness of
remote sensing and GIS techniques in monitoring urban
growth dynamics providing valuable insights for
policymakers when developing sustainable land use
policies.
4. Findings
The findings of the research suggest that remote sensing
and GIS techniques along with accurate data collection,
forecasting, and evaluation techniques, are essential for
effective land use and cover management. The integration
of machine learning and artificial intelligence techniques
can provide valuable insights into the spatial patterns and
dynamics of land use changes, while citizen science and
participatory approaches can enhance stakeholder
engagement and promote sustainable urban planning and
management
The integration of machine learning and artificial
intelligence techniques in land use and cover management
has the potential to provide more accurate and
comprehensive insights into spatial patterns and
dynamics of land use changes. Machine learning models
can be trained to automatically detect land use changes
classify land use categories and forecast future scenarios
based on historical data. Additionally machine learning
algorithms can be used to analyse satellite imagery and
other remote sensing data to identify temporal and spatial
changes in land use patterns. By integrating machine
learning techniques with existing land use management
strategies stakeholders can make informed decisions that
lead to more sustainable and resilient urban development.
participatory approaches can also play a crucial role in
enhancing the accuracy and sustainability of land use
management decisions
There is a lack of coordination in planning and
implementation of growth management strategies for
effective land use and cover management. While advanced
techniques such as statistical and regression models,
supervised and unsupervised classification methods, and
evaluation techniques such as AHP and multi-criteria
evaluation can aid in effective land use management, the
lack of coordination among stakeholders can hinder the
implementation of these strategies. Therefore, further
research can focus on developing innovative approaches
for stakeholder engagement and collaboration in growth
management planning and implementation. Another
research gap is the need to explore the use of advanced
software such as MOLUSCE and SLEUTH for micro-studies
with smaller areas. While these software are commonly
used in larger area studies, they can also be applied in
micro-studies to enhance the accuracy and sustainability
of land use management decisions. Additionally, there is a
need to explore the use of satellite imagery in micro-
studies, as this can provide more detailed and accurate
data for land use mapping and management. Further
research can focus on developing and testing these
approaches in micro-studies to evaluate their
effectiveness in enhancing land use and cover
management.
3. CONCLUSIONS
In conclusion, rapid urbanization has led to significant
changes in the landscape structure and had negative
consequences on the environment and ecology. Urban
policy and sustainable development plans are required to
address these issues by integrating methods that take into
consideration the spatial and temporal scale of changes in
the processes affecting the city's development. Land use
changes caused by ongoing urban sprawl processes are
among the most significant and frequently irreversible
types of environmental change that have an impact on
how we live.
To prevent demographic suburbanization and land use
changes, it is important to identify and implement land
uses that will meet community needs while securing
resources for the future. By studying various models,
methods and indicators from literature and case studies.
we can plan for land use and demographic changes in urba
areas to create a properly planned city in the future.
Geographic information system tools can be used to
analyse future land use models which can lead the way
forward in addressing the challenges of urbanization. The
implementation of these tools and techniques can help us
to create sustainable cities that are environmentally
friendly and provide a high quality of life for their
residents. It is therefore essential that we continue to
study and apply the latest urban planning approaches and
technologies to address the challenges of urbanization.
REFERENCES
[1] H.A. Bharath, T. R. (2017). Modelling urban dynamics
in rapidly urbanising Indian cities. The Egyptian
Journal of Remote Sensing and Space Sciences.
[2] Bielecka, E. (2020). GIS Spatial Analysis Modeling for
Land Use Change. geosciences.
[3] Pandi, D. (2021). Assessment of Land Use and Land
Cover Dynamics. Environmental studies, Pol. J. Environ.
Stud. Vol. 31, No. 3 (2022), 2779-2786.
[4] dawid, W. (2022). GIS-based land cover analysis and
prediction based on open-source software and data.
Sciendo, pp. 75–86.

[5] Shen, S. (2021). GIS-based Logic Scoring of Preference
method for urban densification suitability analysis.
Computers, Environment and Urban System.
[6] Pradeeep, P. S. (2019). Land use and Land Cover
Analysis of Nellore . International Journal of
Engineering Research & Technology, ISSN: 2278-0181.
[7] Roy, B. (2021). Monitoring urban growth dynamics
using remote sensing and GIS. The Egyptian Journal of
Remote Sensing and Space Sciences, The Egyptian
Journal of Remote Sensing and Space Sciences 24
(2021) 221–230.
[8] Divya, Y. (2020). Color slices analysis of land use
changes due to urbanization in a city . Modeling Earth
Systems and Environment.
[9] Stokowska, A. (2021). Spatial-Temporal Land Use and
Land Cover Changes in Urban Areas Using Remote
Sensing Images and GIS Analysis. geosciences,
Geosciences 2021, 11, 312.

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